Sciweavers

Share
ACL
2007

Sparse Information Extraction: Unsupervised Language Models to the Rescue

9 years 1 months ago
Sparse Information Extraction: Unsupervised Language Models to the Rescue
Even in a massive corpus such as the Web, a substantial fraction of extractions appear infrequently. This paper shows how to assess the correctness of sparse extractions by utilizing unsupervised language models. The REALM system, which combines HMMbased and n-gram-based language models, ranks candidate extractions by the likelihood that they are correct. Our experiments show that REALM reduces extraction error by 39%, on average, when compared with previous work. Because REALM pre-computes language models based on its corpus and does not require any hand-tagged seeds, it is far more scalable than approaches that learn models for each individual relation from handtagged data. Thus, REALM is ideally suited for open information extraction where the relations of interest are not specified in advance and their number is potentially vast.
Doug Downey, Stefan Schoenmackers, Oren Etzioni
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ACL
Authors Doug Downey, Stefan Schoenmackers, Oren Etzioni
Comments (0)
books